Nikiforidis G C, Sakellaropoulos G C
Department of Medical Physics, School of Medicine, University of Patras, Greece.
Med Inform (Lond). 1998 Jan-Mar;23(1):1-18. doi: 10.3109/14639239809001387.
The present study concerns the construction and operation of a Bayesian analytical system, namely a Bayesian belief network (BBN) for the prognosis at 24 h of head-injured patients of the intensive care unit. The construction of a BBN incorporates the maintenance of a large database including all the critical variables corresponding to the specific clinical domain. This database is processed to provide the necessary libraries of conditional probability values. BBNs permit the combination of prognostic evidence in a cumulative manner and provide a quantitative measure of certainty in the final decision. The user views the changes at each step, thus being capable of deciding upon the necessary pieces of information in order to reach a certain belief threshold. The system produces results that are compatible with the opinions of medical experts regarding the prognosis of patients exhibiting certain patterns of clinical or laboratory data.
本研究涉及一个贝叶斯分析系统的构建与运行，即用于重症监护病房头部受伤患者24小时预后的贝叶斯信念网络（BBN）。贝叶斯信念网络的构建包括维护一个大型数据库，该数据库包含与特定临床领域相对应的所有关键变量。对这个数据库进行处理，以提供必要的条件概率值库。贝叶斯信念网络允许以累积方式组合预后证据，并在最终决策中提供确定性的定量度量。用户可以查看每一步的变化，从而能够决定获取必要的信息片段，以达到一定的置信阈值。该系统产生的结果与医学专家对表现出特定临床或实验室数据模式的患者预后的意见相一致。
